1. Technical Field
The invention relates to home energy management systems. More particularly, the invention relates to a method and apparatus for incorporating and applying advanced analytics into a home energy management system for providing detailed usage information and energy saving tips, among other relevant data.
2. Description of the Background Art
Home energy management is a newly emerging market with widespread deployment of smart grid infrastructure. A smart grid allows two-way flow of energy and information between an appliance, such as a refrigerator, and a controller, such as a processor residing on a smart meter or even on a server. As well, a smart grid allows two-way flow of information between smart meters and utilities.
Smart Meter
A smart meter is an advanced meter at a consumption site, can measure utility consumption, e.g. consumption of electricity, at the site and is in communication with the utility company at another site, such that the smart meter can send the consumption-related data to the utility company for billing and other purposes. A smart grid infrastructure is the associated network structure that typically includes the smart meter.
Most smart meters have chips embedded, such as a Zigbee chip (“Zigbee”), by Zigbee Alliance. Other communication devices can communicate with smart meters by powerline networking and wireless local area network (WLAN) protocols. However, the Zigbee based home area network (HAN) protocol seems to be becoming standard.
HAN Appliances
Some homes have appliances that are HAN appliances. HAN appliances or devices are appliances/devices that connect to home area networks and can communicate with central controllers and/or smart meters. Information, such as energy usage, energy price, and so forth, can be shared among the devices. As well, HAN appliances can be controlled from a host terminal when such functionality exists. HAN appliances are smart appliances and may also be configured with Zigbee, WLAN, WiFi, etc., communication capability.
Home Energy Management System Background
Home energy management systems have been found to benefit homeowners by providing energy saving measures through usage information feedback and analysis. The home energy management system may become more valuable as energy price increases and as time-of-use (TOU) pricing is implemented. Thus, detailed information may be important in providing quantitative energy saving measures, changing consumer behavior, and diagnosing the efficiency of electricity usage.
Some current approaches for obtaining such detailed information about home energy usage include a full-instrumentation approach. By the full-instrumentation approach, whole houses, individual appliances, and/or wall outlets are instrumented and connected to a home area network to collect point-of-usage data.
Some pros and cons to the full-instrumentation approach are as follows:                Pro: accurate, automatic control capability.        Con: expensive, e.g. >=$300 per house, extensive installation or retrofit required.        
That is, the full-instrumentation approach has been found to be too expensive for many home owners and that pay-back may take longer than 10 years. Thus, a low cost home energy management system may be a key in opening up a mass home energy management market.
Some current companies on the market providing home area network solutions and/or HAN devices/solutions include Control4, iControl, Tendril and EnergyHub. OPOWER, Arlington, Va. and Google PowerMeter by Google organize meter-level electricity data and provide visualizations with limited analysis.
M. R. Durling; Z. Ren, N. Visnevski, and L. E. Ray, Cognitive electric power meter, U.S. Pat. No. 7,693,670 (Apr. 6, 2010) disclose a transient pattern recognition approach to recognize an electric load, embedded in the meter itself. However, certain limitations of such approach are as follows, in no particular order:                With transient recognition, an electric load can be ‘detected’. However, its power consumption is difficult to estimate, unless power consumption is constant right after the turn-on transient.        Faster sampling, e.g. greater than or equal to 1 Hz, is required to recognize a transient pattern, implying this type of algorithm can not be run with meter level data as most meter chips output power data at approximately 1 Hz internally. Instead, a new type of meter is required that can run the algorithm at chip level because current smart meters can not run this type of algorithm.        It requires the cognition capability to be embedded as it requires high sampling.        Transient pattern recognition requires all possible pattern exemplars to be preloaded to meter memory. The number of required pattern exemplars can be prohibitively enormous, as different appliances, different makers/models and different usage create different transient patterns. Also, an exemplar pattern collected in a laboratory environment will be different from a home where an appliance is subject to different usage and aging conditions. Transmission characteristics also play a role in creating a transient pattern in a high frequency sampling environment.        